Add documentation for worker pools

docs/userguide/index.rst

@@ -23,6 +23,7 @@
     optimizing
     debugging
     concurrency/index
+    workerpool
     signals
     testing
     extending

docs/userguide/wokerpool.rst

@@ -0,0 +1,105 @@
+.. _worker-pool:
+
+==============================
+ Different type of Worker Pool
+==============================
+
+.. contents::
+    :local:
+
+.. _worker-pool-introduction:
+
+Introduction
+============
+The concept of a worker pool involves the utilization of a group of worker processes (or threads) 
+to execute tasks in parallel. A worker pool constitutes of several worker instances, each capable 
+of picking up and executing tasks independently. This model provides several benefits which enhance 
+the performance and flexibility of a Celery-based application.
+
+Different type of worker pools
+==============================
+There are four different types of worker pools available in Celery.
+
+.. _worker-pool-prefork:
+
+Prefork Pool
+------------
+The default execution pool in Celery is the prefork pool, which leverages the multiprocessing module of 
+Python to efficiently manage worker processes. This prefork pool model is particularly suited for CPU-bound tasks, 
+as it spawns multiple worker processes that can run in parallel, each taking advantage of individual CPU cores. 
+This multi-process approach is one of the reasons why the prefork pool delivers high performance and stability, 
+making it an optimal choice for many use cases.
+
+The concurrency level for CPU-bound tasks is optimally determined by the number of CPU cores present on the system, 
+as this sets the limit for how many processes can effectively run in parallel. In recognition of this constraint, 
+Celery automatically defaults to the number of available CPU cores to set its concurrency level when the `--concurrency` 
+option is not explicitly specified by the user.
+
+
+.. code-block:: console
+
+    $ celery worker -A proj
+    # Or
+    $ celery worker -A proj --pool=prefork
+
+
+.. _worker-pool-thread:
+
+Thread Pool
+-----------
+Thread pool is a special type of worker pool that utilizes Python's ThreadPoolExecutor.
+This type of pool creates genuine OS-level threads, which are managed by the operating system's kernel.
+This thread pool model is particularly suited for I/O-bound tasks, as it spawns multiple worker threads that can run 
+in parallel. A task is considered I/O-bound when it primarily awaits the completion of Input/Output operations.
+
+Celery automatically set the number of available CPU cores as default concurrency when the `--concurrency` option is not explicitly specified by the user.
+
+.. code-block:: console
+
+    $ celery worker -A proj --pool=threads
+
+
+.. _worker-pool-eventlet:
+
+Eventlet and Gevent Pool
+------------------------
+Eventlet and gevent are both lightweight concurrency libraries for Python that provide a framework for asynchronous networking. In the context of Celery, they offer alternative worker pool implementations that are well-suited for handling large numbers of I/O-bound tasks.
+
+The Eventlet worker pool uses the Eventlet library to spawn a pool of green threads (also known as "greenlets"). 
+These greenlets are cooperatively scheduled lightweight threads that run on a single OS thread, allowing for high 
+concurrency by switching contexts whenever I/O operations are pending, thus making efficient use of downtime where 
+a traditional thread might simply block.
+
+Similarly, the gevent worker pool leverages the gevent library, which also uses greenlets and a cooperative multitasking model. 
+Gevent includes a fast event loop backed by libev or libuv that can handle many simultaneous connections, providing an effective
+way to run network applications and other I/O-bound tasks with a high degree of concurrency.
+
+Both Eventlet and gevent worker pools offer a scalable solution for network applications or other I/O-bound workloads 
+that involve waiting for external operations, such as HTTP requests or database queries. By enabling non-blocking I/O 
+operations through asynchronous execution, these pools can handle many tasks concurrently within a single process, 
+thus reducing memory overhead and taking full advantage of the event-driven programming model.
+
+.. code-block:: console
+
+    $ celery -A proj worker --pool=eventlet -c 1000
+    $ celery -A proj worker --pool=gevent -c 1000
+
+
+.. _worker-pool-solo:
+
+Solo Pool
+---------
+Solo pool is a type of worker pool in Celery, which means it uses only one worker process.
+This can be useful for debugging and development purposes as it allows you to see 
+the full traceback of any errors that occur during task execution. 
+It's particularly useful when you want to isolate issues related to specific tasks or 
+when you need to ensure that a certain task is executed in isolation. The solo pool is 
+also beneficial for testing, as it provides a controlled environment where you can test 
+your code without the overhead of multiple workers. However, keep in mind that using the 
+solo pool might limit the performance and scalability of your application since only one 
+worker process will be available to handle tasks.
+
+.. code-block:: console
+
+    $ celery worker -A proj --pool=solo
+